Ubuntu 18.04: How to install OpenCV

In this blog post you will learn how to install OpenCV on Ubuntu 18.04.

In the past, I’ve authored a handful of installation guides for Ubuntu:

The folks at Canonical have been working hard.

On April 26, 2018, they’ve released a new Long Term Support (LTS) version of Ubuntu for the community: Ubuntu 18.04 LTS (Bionic Beaver).

Support for Ubuntu 16.04 LTS continues until April 2021 so rest-assured — you don’t have to upgrade your 16.04 OS to continue working on your image processing projects.

That said, if you want to upgrade to Ubuntu 18.04 and use the latest-and-greatest, I think you’ll be quite pleased with the new changes in Ubuntu 18.04.

Let’s get down to business and install OpenCV with Python 3 bindings.

To learn how to stand up your Ubuntu 18.04 system with OpenCV, just keep reading.

Note: While you won’t see an Ubuntu 17.10 specific guide here on my blog (non-LTS), these instructions may work with 17.10 (you’ll just have to proceed at your own risk).

Ubuntu 18.04: How to install OpenCV

One major change in Ubuntu 18.04 is that they’ve dropped Python 2.7 completely.

You can still install Python 2.7 if-needed, but now Python 3 is the default on the OS.

Given that, this guide supports Python 3. If you need Python 2.7 support, read this entire guide first and then check the first question of the Troubleshooting your install (FAQ) section near the bottom of this blog post for some Python 2.7 pointers.

Step #0: Get comfortable — you’ll be using Python 3.6

Let’s familiarize ourselves with Python 3 on Ubuntu 18.04.

To run Python 3 on Ubuntu 18.04, you must call python3  explicitly.

Let’s see which version is installed on our system:

And now, let’s launch a Python 3 shell just to test the waters:

That’s easy enough, so let’s get on with installing OpenCV on Ubuntu 18.04.

Step #1: Install OpenCV dependencies on Ubuntu 18.04

All steps today will be accomplished in the terminal/command line. Before we begin, open a terminal or connect via SSH.

From there, we need to refresh/upgrade the pre-installed packages/libraries with the apt-get package manager:

Followed by installing developer tools:

You most likely already have pkg-config  installed on Ubuntu 18.04, but be sure to include it in the install command for sanity.

Next, we need to install some OpenCV-specific prerequisites. OpenCV is an image processing/computer vision library and therefore it needs to be able to load standard image file formats such as JPEG, PNG, TIFF, etc. The following image I/O packages will allow OpenCV to work with image files:

Similarly, let’s include video I/O packages as we often work with video on the PyImageSearch blog. You’ll need the following packages so you can work with your camera stream and process video files:

OpenCV’s highgui module relies on the GTK library for GUI operations. The highgui module will allow you to create elementary GUIs which display images, handle kepresses/mouse clicks, and create sliders and trackbars. Advanced GUIs should be built with TK, Wx, or QT. See this blog post to learn how to make an OpenCV GUI with TK.

Let’s install GTK:

I always recommend the following two libraries which will optimize various OpenCV functions:

And finally, our last requirement is to install Python 3 headers and libraries:

Step #2: Download the official OpenCV source

At the time of this writing, the most recent release is OpenCV 3.4.1. These instructions should continue to work with future OpenCV 3.x versions as well.

Since we’re continuing to work in the terminal, let’s download the official OpenCV release using wget :

Followed by the opencv_contrib  module:

Note: If your browser is cutting off the full command either use the “<>” button in the toolbar above to expand the code block or copy and paste the following URL: https://github.com/opencv/opencv_contrib/archive/3.4.1.zip

So, what’s the contrib repo?

The contrib repository contains algorithms such as SIFT, SURF, and others. In the past, these implementations were included in the default installation of OpenCV 2.4; however, they were moved beginning with OpenCV 3+.

Modules that are actively being developed and/or modules that are patented (not free for commercial/industry use) are included in the contrib module. SIFT and SURF fall into this category. You can learn more about the thought process behind this move in the following blog post: Where did SIFT and SURF go in OpenCV 3?

Important: Both opencv  and opencv_contrib  versions mush be identical. Notice that both URLs point to 3.4.1. Feel free to install a different version while still using this guide — just be sure to update both URLs.

Now, let’s unzip the archives:

Figure 1: After downloading and unzipping opencv and opencv_contrib, our home directory listing should look similar to what is displayed in the terminal.

Step #3: Configure your Python 3 environment

The first step we’re taking to configure our Python 3 development environment is to install pip, a Python Package Manager.

To install pip, simply enter the following in your terminal:

Making use of virtual environments for Python development

If you are familiar with my blog and install guides therein, the following statement might make me sound like a broken record but I’ll repeat it anyway:

I use both virtualenv and virtualenvwrapper daily and you should too unless you have a very specific reason not to. These two Python packages facilitate creating independent Python environments for your projects.

It is a best practice to use virtual environments.

Why?

Virtual environments allow you to work on your projects in isolation without spinning up resource hogs such as VMs and Docker images (I definitely do use both VirtualBox and Docker — they have their place).

For example, maybe you have a Python + OpenCV project that requires an older version of scikit-learn (v0.14) but you want to keep using the latest version of scikit-learn (0.19) for all of your newer projects.

Using virtual environments, you could handle these two software version dependencies separately, something that is not possible using just the system install of Python.

If you would like more information about Python virtual environments take a look at this article on RealPython or read the first half of the this blog post on PyImageSearch.

Let’s go ahead and install   virtualenv  and virtualenvwrapper  now:

To finish the install we need to update our  ~/.bashrc  file.

Using a terminal text editor such as vi / vim  or nano , add the following lines to your ~/.bashrc :

Alternatively, you can append the lines directly via bash commands:

Next, source the ~/.bashrc  file:

Creating a virtual environment to hold OpenCV and additional packages

Ok, while that may have seemed like a lot of work, we’re at the point where we can create your Python 3 virtual environment for OpenCV:

This line simply creates a Python 3 virtual environment named cv . You can name your environment(s) whatever you’d like — I like to keep them short and sweet while also providing enough information so I’ll remember what they are for. You can have as many virtual environments on your system as you’d like!

Let’s verify that we’re in the cv environment by using the workon command:

Figure 2 shows what your terminal will look like (assuming you haven’t changed any bash prompt settings):

Figure 2: If you see the (cv) at the beginning of the bash prompt, then your virtual environment is active and you’re working “inside” the environment. You can now safely install OpenCV with correct Python bindings.

Install NumPy in your environment

Let’s install our first package into the environment: NumPy. NumPy is a requirement for working with Python and OpenCV. We simply use pip (while the cv  Python virtual environment is active):

Step #4: Configure and compile OpenCV for Ubuntu 18.04

Now we’re moving. We’re ready to compile and install OpenCV.

Before we begin though, let’s ensure that we’re in the cv virtual environment:

It is very important that the virtual environment is active (you are “inside” the virtual environment) which is why I keep reiterating it. If you are not in the cv  Python virtual environment before moving on to the next step your build files will not be generated properly.

Configure OpenCV with CMake

Let’s set up our OpenCV build using cmake :

I always recommend that you scroll through the CMake output and check to see if anything looks out of the ordinary. You won’t see a “YES” marked next to every setting — that is normal. Be sure you don’t see any errors or your compile may fail (warnings are okay).

Figure 3: To compile OpenCV for Ubuntu 18.04, we make use of CMake. The CMake tool will configure settings prior to compilation.

Take a moment to notice that only “Python 3” section is shown in the CMake output on Ubuntu 18.04 in Figure 3. This is by design as we are only compiling OpenCV with Python 3 support.

Note: If you are encountering problems related to stdlib.h: No such file or directory  during either the cmake  or make  phase of this tutorial you’ll also need to include the following option to CMake:   -D ENABLE_PRECOMPILED_HEADERS=OFF . In this case I would suggest deleting your build directory, re-creating it, and then re-running cmake  with the above option included. This will resolve the stdlib.h  error.

Compiling OpenCV on Ubuntu 18.04

Let’s compile OpenCV using make .

Depending on the number of processors/cores, you may be able to reduce compile time by altering the flag in the command. My computer has 4 cores, so I am using the -j4  flag. You can update the numeral or leave the flag off altogether:

Figure 4: To compile OpenCV with Python 3 on Ubuntu 18.04, we use make. Using make compiles OpenCV from source and is preferred over using package managers for installing OpenCV.

This process may take 30 minutes or longer, so go for a nice walk if you are able.

If your compile chokes and hangs, it may be due to a threading race condition. In the event you run into this problem, simply delete your build  directory, recreate it, and re-run cmake  and make . This time do not include the flag next to make .

Installing and verifying OpenCV

Upon a successful, 100% complete compile you can now install OpenCV:

To verify the install, sometimes I like to enter the following command in the terminal:

Step #5: Finish your Python+ OpenCV + Ubuntu 18.04 install

We’ve reached the last lap of the race so stick with it.

At this point, your Python 3 bindings for OpenCV should reside in the following folder:

Let’s rename them to simply cv2.so :

Our last step is to sym-link our OpenCV cv2.so  bindings into our cv  virtual environment:

Step #6: Testing your OpenCV install on Ubuntu 18.04

The race is done, but let’s verify that we’re firing on all cylinders.

To verify that our OpenCV + Ubuntu install is complete, I like to launch Python, import OpenCV, and query for the version (this is useful for sanity if you have multiple versions of OpenCV installed as well):

Here’s what it looks like on my system:

Figure 5: To verify that OpenCV is correctly installed and configured in our Python 3 virtual environment, I like to run the Python interpreter in the terminal. From there you can import OpenCV (cv2) and verify the version number matches what you intended to install.

Optionally, at this point, you can safely delete the zips and directories in your home folder:

Troubleshooting your install (FAQ)

In this section, I address some of the common questions, problems, and issues that arise when installing OpenCV 3 with Python 3 on Ubuntu 18.04 LTS.

Q. Where is Python 2.7 on Ubuntu 18.04?

A. Python 3 is the default and what comes with Ubuntu 18.04. Python 2.7 users can manually install Python 2.7 at the end of Step #1:

From there, when you create your virtual environment in Step #3, first install pip for Python 2.7:

And then (also in Step #3) when you make your virtual environment, simply use the relevant Python version flag:

From there everything should be the same.

Q. Why can’t I just pip to install OpenCV?

A. There are a number of pip-installable versions of OpenCV available depending on your operating system and architecture. The problem you may run into is that they may be compiled without various optimizations, image I/O support, video I/O support, and opencv_contrib  support. Use them — but use them at your own risk. This tutorial is meant to give you the full install of OpenCV on Ubuntu 18.04 while giving you complete control over the compile.

Q. When I execute mkvirtualenv  or workon , I encounter a “command not found error”.

A. There a number of reasons why you would be seeing this error message, all of come from to Step #3:

  1. First, make sure you have installed virtualenv  and virtualenvwrapper  properly using the pip  package manager. Verify by running pip freeze , and ensure that you see both virtualenv  and virtualenvwrapper  in the list of installed packages.
  2. Your ~/.bashrc  file may have mistakes. View the contents of your ~/.bashrc  file to see the proper export  and source  commands are present (check Step #3 for the commands that should be appended to ~/.bashrc ).
  3. You may have forgotten to source  your ~/.bashrc . Make sure you run  source ~/.bashrc  after editing it to ensure you have access to the mkvirtualenv  and workon  commands.

Q. When I open a new terminal, logout, or reboot my Ubuntu system, I cannot execute the mkvirtualenv  or workon  commands.

A. See #2 from the previous question.

Q. When I try to import OpenCV, I encounter this message:  Import Error: No module named cv2 .

A. There are multiple reasons this could be happening and unfortunately, it is hard to diagnose. I recommend the following suggestions to help diagnose and resolve the error:

  1. Make sure your  cv  virtual environment is active by using the workon cv  command. If this command gives you an error, then see the first question in this FAQ.
  2. Try investigating the contents of the site-packages  directory in your cv  virtual environment. You can find the site-packages  directory in ~/.virtualenvs/cv/lib/python3.6/site-packages/  depending on your Python version. Make sure (1) there is a cv2.so  file in the  site-packages  directory and (2) it’s properly sym-linked to a valid file.
  3. Be sure to check the site-packages  (and even dist-packages ) directory for the system install of Python located in /usr/local/lib/python3.6/site-packages/ , respectively. Ideally, you should have a cv2.so  file there.
  4. As a last resort, check in your build/lib  directory of your OpenCV build. There should be a cv2.so  file there (if both cmake  and make  executed without error). If the cv2.so  file is present, manually copy it into both the system site-packages  directory as well as the site-packages  directory for the cv  virtual environment.

What’s possible with OpenCV?

The possibilities with OpenCV are truly endless.

Perhaps you’re interested in tracking object movement:

Figure 6: We can use OpenCV to track the motion of objects in real time!

Or maybe you’d like to use OpenCV + deep learning to detect faces in images/video? Trust me, it’s easier than it sounds!

Figure 7: Detecting faces in realtime with OpenCV is easier than it sounds.

Or maybe you’re looking for a beginner-level book, designed for newcomers to the world of computer vision?

If so, look no further than my book, Practical Python and OpenCV + Case Studies.

Figure 8: Practical Python and OpenCV + Case Studies is a book meant for beginners but it is also useful for those that want to solidify their knowledge of the fundamentals. Be sure to give it a read!

Inside this book you’ll:

  • Master the fundamentals of computer vision and image processing, taught on a fun, hands-on manner.
  • Learn by doing, including visual examples and lots of code (including line-by-line reviews, ensuring you understand exactly what’s going on).
  • Implement case studies, including face detection, object trackinghandwriting recognition, and more!

To learn more about the book (and how it can help you learn computer vision + OpenCV), just click here!

Summary

Today we installed OpenCV 3 with Python 3 bindings on Ubuntu 18.04 LTS.

I hope you found these instructions helpful on getting your own Ubuntu 18.04 machine configured with OpenCV 3.

If you’re interested in learning more about OpenCV, computer vision, and image processing, be sure to enter your email address in the form below to be notified when new blog posts + tutorials are published!

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49 Responses to Ubuntu 18.04: How to install OpenCV

  1. El Barto May 28, 2018 at 10:56 am #

    Hi Adrian! Is there a certain reason why you don’t clone the OpenCV repo instead of fetching as an archive?

    • Adrian Rosebrock May 28, 2018 at 11:31 am #

      You could do either, but downloading the official release archive tends to be easier for those who are not used to installing and configuring OpenCV on their systems (less commands to execute hear typically means less chance of causing a problem). If you cloned down the original repo the download would take longer and you would have the added step of checking out the specific release.

  2. Sarkar May 28, 2018 at 11:32 am #

    Hey Adrian, thanks for another awesome guide. Is it possible to use conda (instead of pip) in the above guide?

    thanks

    • Adrian Rosebrock May 28, 2018 at 4:22 pm #

      You should be able to but I haven’t tested with Anaconda (I’m not an Anaconda user). The most important part will be during the “cmake” step where you’ll want to make sure your “Python 3” libraries, interpreter, etc. point to your Anaconda version, not your system version — this is a mistake I’ve seen others make.

  3. Adam May 28, 2018 at 11:33 am #

    Hey Adrian, thanks a lot for another awesome tutorial!

    1) Are you planning to release another one for the lucky GPU owners? 😉
    2) Are you aware of any compatibility issues for those who are into your Deep Leaning book? 😉

    Cheers,
    Adam

    • Adrian Rosebrock May 28, 2018 at 4:21 pm #

      1. I’m not sure what you mean. Are you asking for specific deep learning library install instructions?

      2. I am not aware of any compatibility issues but I also haven’t performed exhaustive experiments on Ubuntu 18.04 either.

      • Adam May 28, 2018 at 5:01 pm #

        1. Yes. I was wondering if the instructions for Ubuntu 18.04 deep learning installation will be similar to 16.04. It took me literally weeks to get all lib versions (TF, cuDNN, Cuda) to work together on 16.04. It was a pain and I failed miserably when tried to wrap it all inside a clean Docker image ;/

        2. That’s cool Adrian

      • Adam May 28, 2018 at 5:02 pm #

        Ooops, I can see your recommendation to stick to 16.04 for deep learning, sorry.

        • Adrian Rosebrock May 29, 2018 at 4:17 pm #

          When it comes to deep learning environments, if it’s not broken, don’t fix it. Ubuntu 16.04 is perfectly legitimate for deep learning work. I would not be chomping at the bit to immediately switch to Ubuntu 18.04 for deep learning unless there was a very specific reason for doing so.

  4. Thúlio May 28, 2018 at 12:31 pm #

    Hello Adrian! I have a question about the new Ubuntu release. Have you been using Ubuntu 18.04 for deep learning? I’ve installed it and I’m planning on using TensorFlow and MXNet with GPU support enabled, but I’m afraid I was too eager to try the new release and didn’t think about the lack of support for those packages since 18.04 is too new. Should I have sticked with 16.04? Or are TF and MXNet already good to go on 18.04?

    • Adrian Rosebrock May 28, 2018 at 4:19 pm #

      As far as deep learning goes I would recommend sticking with Ubuntu 16.04 for the time being. Ubuntu 16.04 is more “mature” in terms of support of deep learning libraries. Keep in mind that Ubuntu 18.04 is essentially brand new and would be more likely of encountering issues. Deep learning libraries change rapidly though so I wouldn’t get too concerned about it if you’re already on 18.04.

  5. Swan May 28, 2018 at 2:58 pm #

    Hi Adrian!
    Why do not you use pip install opencv-python ?

    • Swan May 28, 2018 at 3:00 pm #

      Sorry, I’m not seen the FAQ section

      • Adrian Rosebrock May 28, 2018 at 4:18 pm #

        No worries!

  6. Doug May 29, 2018 at 12:10 am #

    This article is excellent as always and timely for me, as I spent the last few days making opencv3.4.1 with dnn_modern and python 2.7 and 3.6 libs in a ubuntu16.04 docker. The long build time you noted above – combined with my copious mistakes insured many joyous repetitions of the cmake/make bits, until I got the opencv cmake build options just right. (And I needed? to build python cv libs and load them before I could see if they could find all their shared libs, in turn.) I’ve tried virtualenv but it was just too easy for me to break my ubuntu desktop with it. The docker approach seems to make up for my sloppiness with virtualenv. I often need a bib when I eat, though, so that might be a pattern..

    • Adrian Rosebrock May 29, 2018 at 4:15 pm #

      Congrats on getting OpenCV installed on your Ubuntu system, Doug! I agree that Docker is a good use case here. Your rational for finding it too easy to break your main system is part of the reason why I really like cloud-based systems. Configure it once, snapshot it, and then delete + spin up a new instance if you ever break it!

  7. Benjamin May 29, 2018 at 8:18 am #

    Hi Adrian,

    I am curious to know why you did not include the optimization in the cmake commands as previously referred to in your optimization post: https://www.pyimagesearch.com/2017/10/09/optimizing-opencv-on-the-raspberry-pi/

    The two commands i am referring to are:
    -D ENABLE_NEON=ON \
    -D ENABLE_VFPV3=ON \

    Really enjoying what you are putting out into the community.

    Regards
    Ben

    • Adrian Rosebrock May 29, 2018 at 4:14 pm #

      Those optimizations are for ARM processors. Most Ubuntu users will likely be on non-ARM chipsets. If you are installing Ubuntu + OpenCV on an ARM processor you can of course use them.

  8. Ben May 31, 2018 at 9:35 am #

    Hi Adrian,

    I am new to your blog but I have been working with success so far on the recent steps 1 & 2 of the Pokemon project (microsoft bing search & keras deep learning)…

    Everything seems to be working for me with Anaconda 3.6 and Linux Mint 18.3 Cinnamon 64 bit with installing/using the deep learning libraries..

    Im also new to Linux OS and an IT person recommended Mint to me for a place to start…

    Would you recommend at all changing to Ubuntu at some point in time? Is there anything special about Linux Mint and using Anaconda? At some point in time I may actually have enough experience to require virtual environments with different versions of software…

    Thanks much… Cheers.
    Ben

    • Adrian Rosebrock June 5, 2018 at 8:37 am #

      I’ll be honest, I don’t know much about Linux Mint so I don’t think I’m qualified to say whether you should be using Ubuntu over Linux Mint. That said, I Ubuntu is often used for many deep learning projects and it’s very user friendly. I would recommend it if you are using Linux systems.

  9. Chris June 1, 2018 at 12:59 pm #

    Thanks, Adrian. This is a very good tutorial for me to implement opencv on my new computer. I have a question now, how can I work in jupyter notebook and use this cv virtual environment. Thanks very much.

    • Adrian Rosebrock June 5, 2018 at 8:22 am #

      You would need to install Jupyter into the “cv” Python virtual environment:

  10. Antony Smith June 5, 2018 at 8:10 am #

    Hi Adrian,
    I bapassed the virtual env installation, but upon testing the installation (after all else installing correctly), I’m getting the Error:

    ImportError: /usr/local/lib/python3.6/site-packages/cv2.so: undefined symbol: _ZTIN2cv3dnn19experimental_dnn_v35LayerE

    • Antony Smith June 5, 2018 at 8:13 am #

      Okay nevermind, my bad.
      Just opened a new terminal and retried and all is right with the world…
      Thanks man!
      You’re the best!

      • Adrian Rosebrock June 5, 2018 at 8:25 am #

        Congrats on resolving the issue, Anthony! 🙂

  11. Andres June 5, 2018 at 8:25 am #

    hi Adrian

    Iḿ having trouble sinvce I can find my site-packages folder in the path you say, there is only the dist-packages directory in that path. I don’t know where to look for it or if it is suposed to be only there.

    thank you in advance

    Andres

    • Adrian Rosebrock June 5, 2018 at 8:39 am #

      I’m not sure why you would not have a site-packages directory and only a dist-packages directory. They should both be in the same subdirectory. Which step are you on?

      • Andres June 5, 2018 at 8:42 am #

        I’m on step 5 making the link, all the make an make install has finished without error

        • Adrian Rosebrock June 7, 2018 at 3:18 pm #

          If the “make install” worked you should check your “dist-packages” directory for the “cv2.so” bindings instead.

  12. Andres June 6, 2018 at 8:41 am #

    Hi Adrian

    I have changed to Ubuntu 16.04 since im going tu use DNN and it seems is more reliable in that version, I have a doubt about CUDA integration with my project because I’m having trouble integrating the library in opencv, since im going to use tensorflow which one is better to compile CUDA with? or i will need it with booth of them?

    • Adrian Rosebrock June 7, 2018 at 3:09 pm #

      You can use OpenCV with CUDA but it really depends on what you are doing. If your goal is to be developing DNNs you should install TensorFlow/Keras with CUDA support. OpenCV can be used to make predictions with some DNNs but for training you should be using TensorFlow/Keras.

  13. Anand Setlur June 8, 2018 at 5:13 pm #

    Hi Adrian, I had to modify your cmake script to get it to work with CUDA9.0 in Ubuntu18.04 to use gcc-6 and fix some other build errors by specifying the CUDA_NVCC_FLAGS and PYTHON2_EXECUTABLE options

    export CC=/usr/bin/gcc-6
    export CXX=/usr/bin/g++-6
    mkdir -p build
    cd build
    cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CUDA_NVCC_FLAGS=–expt-relaxed-constexpr \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D INSTALL_C_EXAMPLES=OFF \
    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.4.1/modules \
    -D PYTHON_EXECUTABLE=~/.virtualenvs/cv3/bin/python \
    -D PYTHON2_EXECUTABLE=/usr/bin/python2 \
    -D BUILD_EXAMPLES=ON ..
    make -j4

    • Adrian Rosebrock June 13, 2018 at 6:11 am #

      Thank you for sharing, Anand!

  14. artnect June 13, 2018 at 4:17 am #

    Hi Adrian

    I have gotten several failed process on building open cv using cmake

    can you help me on that ?

    the log file is too long i can’t post it here.

    • Adrian Rosebrock June 13, 2018 at 5:23 am #

      Can you create a GitHub Gist and link to it?

        • Adrian Rosebrock June 15, 2018 at 12:44 pm #

          Looking at your cmake output I don’t see any issues. Your “Python 3” section is correctly filled out and your build files have been generated. Keep in mind that many OpenCV configurations are optional. Just because cmake reports “not found” doesn’t mean it’s an error. You should be able to go ahead and compile OpenCV.

  15. Skanda Kumar June 14, 2018 at 1:59 pm #

    Hi Adrian, I am a novice user of OpenCV. My supervisor at my internship was very specific about installing OpenCV version 2.4. I installed the latest version of Ubuntu(18.04) in my system and I am trying to install the the required version of OpenCV but I am unable to. This was the most detailed post I could find on the internet and I would be grateful if you can help with getting OpenCV 2.4 running in my Ubuntu 18.04. Thanks in advance 🙂

    • Adrian Rosebrock June 14, 2018 at 3:58 pm #

      Oh man, I imagine trying to install OpenCV 2.4 on Ubuntu 18.04 would be quite a pain. It’s a hack but you may want to consider installing a VM with a much older version of Ubuntu 10.04/12.04 and then following the instructions from a previous previous Raspbian tutorial (Ubuntu and Raspbian are both Debian based). I do not know offhand what changes would need to be made for this tutorial and OpenCV 2.4 but I imagine there would be a few, mostly related to the specific apt-get packages installed. Best of luck with it!

  16. Max Lee June 14, 2018 at 2:28 pm #

    After following instructions, to specify here, I found that contrib modules, while being built, weren’t actually getting installed… after digging around, found that you need to modify the cmake command where the extra modules are specified, to add a target for them.

    Updated cmake command:
    cmake -D CMAKE_BUILD_TYPE=RELEASE \
    -D CMAKE_INSTALL_PREFIX=/usr/local \
    -D INSTALL_PYTHON_EXAMPLES=ON \
    -D INSTALL_C_EXAMPLES=OFF \
    -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.4.1/modules ~/opencv-3.4.1\
    -D PYTHON_EXECUTABLE=~/.virtualenvs/cv/bin/python \
    -D BUILD_EXAMPLES=ON ..

    This then allowed me to use the contrib modules for a few things that I was also compiling in C++.

  17. Josef Matondang June 15, 2018 at 2:52 am #

    Hello Adrian, do you know how to change the Python for build from python2.7 to the environment’s python3

    — Python 3:
    — Interpreter: /home/josefmtd/.virtualenvs/cv/bin/python3 (ver 3.6.5)
    — Libraries: /usr/lib/x86_64-linux-gnu/libpython3.6m.so (ver 3.6.5)
    — numpy: /home/josefmtd/.virtualenvs/cv/lib/python3.6/site-packages/numpy/core/include (ver 1.14.5)
    — packages path: lib/python3.6/site-packages

    — Python (for build): /usr/bin/python2.7

    • Adrian Rosebrock June 15, 2018 at 12:01 pm #

      The “Python (for build)” section is buggy and will not be correctly filled in. Ignore it. As long as your “Python 3” section is properly filled in you’ll be able to build the OpenCV bindings.

    • Nathan Sieracki June 17, 2018 at 7:39 pm #

      Josef,

      I encountered the same issue. Adrian mentioned this is of no consequence to the buid as long as the Python 3 section is filled in, but I resolved the Python (for build) issue by executing the below cmake tags, which pointed the python executable directory to the Python3, (note the final tag):

      cmake -D CMAKE_BUILD_TYPE=RELEASE \
      -D CMAKE_INSTALL_PREFIX=/usr/local \
      -D INSTALL_PYTHON_EXAMPLES=ON \
      -D INSTALL_C_EXAMPLES=OFF \
      -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.4.1/modules \
      -D PYTHON_EXECUTABLE=~/.virtualenvs/cv/bin/python3 \
      -D BUILD_EXAMPLES=ON
      -D BUILD_OPENCV_PYTHON3=YES

  18. jeremy June 17, 2018 at 12:31 am #

    Hi Adrian, i’ve followed your steps like a recipe book, but i have no idea why my Python (for build) is NO after finishing the cmake.

    i’ve tried other methods but it isn’t working.

    i am able to access python3.6.5 by typing ‘python’ in my terminal. 🙁

    • jeremy June 17, 2018 at 2:33 am #

      sorry i got it to work already.

  19. jeremy June 17, 2018 at 2:33 am #

    turns out that i forgot to change my envs name

    • Adrian Rosebrock June 19, 2018 at 8:55 am #

      Congrats on resolving the issue, Jeremy!

  20. CrashDummy June 20, 2018 at 8:46 pm #

    Hi Adrian,

    Just setup Ubuntu 18.04 and your blog post email popped up in my inbox, amazing 🙂 !

    Sort of a side question, I’ve recently stumble across “pipenv” for setting up pip virtual environments, was wondering have you tried it and would love to hear your comments!

    • Adrian Rosebrock June 21, 2018 at 5:37 am #

      I’ve played with pipenv on one system but I personally haven’t put it through the ringer yet. I still really like virtualenv.

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